library(data.table)
library(tidyverse)
library(dplyr)
library(plotly)
library(DT)
library(knitr)
plot_ly() and ggplotly() functionsplot_geo()DataTableWe will work with the COVID data presented in lecture. Recall the dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic. We will explore cases, deaths, and their population normalized values over time to identify trends.
## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
# load COVID state-level data from NYT
### FINISH THE CODE HERE ###
cv_states <- as.data.frame(fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
# load state population data
### FINISH THE CODE HERE ###
state_pops <- as.data.frame(fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE ###
cv_states <- merge(cv_states, state_pops, by = 'state')
head, and tail of the datadim(cv_states)
## [1] 12490 9
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 1 Alabama 2020-08-25 1 117242 2037 1 4887871 96.50939 AL
## 2 Alabama 2020-05-12 1 10464 435 1 4887871 96.50939 AL
## 3 Alabama 2020-09-08 1 133606 2277 1 4887871 96.50939 AL
## 4 Alabama 2020-08-13 1 105557 1890 1 4887871 96.50939 AL
## 5 Alabama 2020-05-25 1 14986 566 1 4887871 96.50939 AL
## 6 Alabama 2020-09-07 1 132973 2276 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 12485 Wyoming 2020-05-12 56 675 7 56 577737 5.950611 WY
## 12486 Wyoming 2020-07-17 56 2069 24 56 577737 5.950611 WY
## 12487 Wyoming 2020-09-10 56 4199 42 56 577737 5.950611 WY
## 12488 Wyoming 2020-09-22 56 5016 49 56 577737 5.950611 WY
## 12489 Wyoming 2020-05-26 56 850 13 56 577737 5.950611 WY
## 12490 Wyoming 2020-08-12 56 3086 29 56 577737 5.950611 WY
str(cv_states)
## 'data.frame': 12490 obs. of 9 variables:
## $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ date : IDate, format: "2020-08-25" "2020-05-12" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 117242 10464 133606 105557 14986 132973 116710 38045 159169 30021 ...
## $ deaths : int 2037 435 2277 1890 566 2276 2024 950 2558 839 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : chr "AL" "AL" "AL" "AL" ...
state and abb into a factor variable# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state variable
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
# format the state abbreviation (abb) variable
### FINISH THE CODE HERE ###
abb_list = unique(cv_states$abb)
cv_states$abb = factor(cv_states$abb, levels = abb_list)
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame': 12490 obs. of 9 variables:
## $ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : Date, format: "2020-03-13" "2020-03-14" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 6 12 23 29 39 51 78 106 131 157 ...
## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 212 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 224 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 219 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 143 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 187 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 49 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 12357 Wyoming 2020-10-22 56 10119 68 56 577737 5.950611 WY
## 12476 Wyoming 2020-10-23 56 10545 68 56 577737 5.950611 WY
## 12345 Wyoming 2020-10-24 56 10805 68 56 577737 5.950611 WY
## 12400 Wyoming 2020-10-25 56 11041 68 56 577737 5.950611 WY
## 12334 Wyoming 2020-10-26 56 11477 77 56 577737 5.950611 WY
## 12457 Wyoming 2020-10-27 56 11806 77 56 577737 5.950611 WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 212 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 224 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 219 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 143 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 187 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 49 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
## state date fips cases
## Washington : 281 Min. :2020-01-21 Min. : 1.00 Min. : 1
## Illinois : 278 1st Qu.:2020-04-30 1st Qu.:16.00 1st Qu.: 3294
## California : 277 Median :2020-06-29 Median :29.00 Median : 20016
## Arizona : 276 Mean :2020-06-29 Mean :29.77 Mean : 66099
## Massachusetts: 270 3rd Qu.:2020-08-28 3rd Qu.:44.00 3rd Qu.: 76358
## Wisconsin : 266 Max. :2020-10-27 Max. :72.00 Max. :923828
## (Other) :10842
## deaths geo_id population pop_density
## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
## 1st Qu.: 75 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 54.956
## Median : 528 Median :29.00 Median : 4468402 Median : 107.860
## Mean : 2282 Mean :29.77 Mean : 6561680 Mean : 420.670
## 3rd Qu.: 2238 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
## Max. :33092 Max. :72.00 Max. :39557045 Max. :11490.120
## NA's :229
## abb
## WA : 281
## IL : 278
## CA : 277
## AZ : 276
## MA : 270
## WI : 266
## (Other):10842
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2020-10-27"
The range of dates starts as early as January 1st until October 27th. The range of cases is very vast with the number of cases being the running grand total from every day. The same goes for the number of deaths as the recorded number of deaths is a running total ranging from 0 to 33,092 deaths.
new_cases and new_deaths and correct outliersAdd variables for new cases, new_cases, and new deaths, new_deaths:
new_cases is equal to the difference between cases on date i and date i-1, starting on date i=2Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updates new_cases and new_deaths
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
cv_subset = cv_subset[order(cv_subset$date),]
# add starting level for new cases and deaths
cv_subset$new_cases = cv_subset$cases[1]
cv_subset$new_deaths = cv_subset$deaths[1]
#### FINISH THE CODE HERE ###
for (j in 2:nrow(cv_subset)) {
cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Inspect outliers in new_cases and new_deaths using plotly
### FINISH THE CODE HERE ###
p1<-ggplot(cv_states, aes(date, new_cases, color = state))+
geom_line()+
geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace
### FINISH THE CODE HERE ###
p2<-ggplot(cv_states, aes(date, new_deaths, color = state))+
geom_line()+
geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
# add starting level for new cases and deaths
cv_subset$cases = cv_subset$cases[1]
cv_subset$deaths = cv_subset$deaths[1]
for (j in 2:nrow(cv_subset)) {
cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
The new_cases line plot visual has recorded cases that are in the negatives, such as the state of Massuchusetts in September 2nd with -7757 cases. For our new_cases variable we do have some extraneous outliers with the state of Texas on September 1st with 22,276 new cases and Georgia on October 5th with 21,349 new cases. For our variabl new_deaths we do have some negative values, which is not obtainable. There are some outliers with the state of New Jersey with 1,877 new deaths on June 25th.
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
per100k = cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k = deaths per 100,000newdeathsper100k = new deaths per 100,000Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture
# add population normalized (by 100,000) counts for each variable
cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),
nsmall=1))
cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),
nsmall=1))
cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),
nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),
nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states%>%
mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
### FINISH THE CODE HERE ###
cv_states_today = cv_states%>%
filter(date == as.Date(max(date)))
plot_ly()plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
hovermode = "compare"# pop_density vs. cases
### FINISH THE CODE HERE ###
cv_states_today%>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter%>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_scatter%>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_scatter%>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
hoverinfo = 'text',
text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep=""),
paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>"))%>%
layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
hovermode = "compare")
ggplotly() and geom_smooth()pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
geom_*() we need here?geom_smooth()
pop_density is a correlate of newdeathsper100k?### FINISH THE CODE HERE ###
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=newdeathsper100k, size=population))+
geom_point()+
geom_smooth(method = 'lm')
ggplotly(p)
For our variables of pop_density on newdeathsper100k we see that there is a slight linear correltation. With the clustering of points towards the lower end of pop_density our linear correlation with the new deaths per 100k may be weak.
naive_CFR for all states over time using plot_ly()
naive_CFR for the states that had a “first peak” in September. How have they changed over time?new_cases and new_deaths together in one plot. Hint: use add_lines()
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
# Line chart for Texas showing new_cases and new_deaths together
### FINISH THE CODE HERE ###
cv_states%>%
filter(state=="Texas")%>%
plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines")%>%
add_lines(x = ~date, y = ~new_deaths, type = 'scatter', mode = 'lines')
The peak of new cases is on September 21st with 22,276 recorded within the United States. The peak of deaths was recorded on July 27th with a record of 1,202. There was about a 2 months time delay difference between the two different peaks.
Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states%>%
select(state, date, new_cases)%>%
filter(date>as.Date("2020-04-01"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2020-04-01"), as.Date("2020-10-01"), by="2 weeks")
### FINISH THE CODE HERE ###
cv_states_mat <- cv_states%>%
select(state, date, new_cases)%>%
filter( date %in% filter_dates )
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
naive_CFR by state on May 1st, 2020naive_CFR by state on most recent datesubplot(). Make sure the shading is for the same range of values (google is your friend for this)### For May 1 2020
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states%>%
filter(date=="2020-05-01")%>%
select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 9
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states')%>%
add_trace(
z = ~naive_CFR, text = ~hover, locations = ~state,
color = ~naive_CFR, colors = 'Purples'
)
fig <- fig%>%
colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig%>%
layout(title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
geo = set_map_details)
fig_May1 <- fig
#############
### For Today
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states_today%>%
select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states')%>%
add_trace(z = ~naive_CFR, text = ~hover, locations = ~state,
color = ~naive_CFR, colors = 'Purples')
fig <- fig%>%
colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig%>%
layout(title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
geo = set_map_details)
fig_Today <- fig
### Plot side by side
### FINISH THE CODE HERE ###
subplot(fig_May1, fig_Today)